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Over the course of the 2016 Presidential campaign, an execrable trend has emerged: reporters who voiced even slightly negative opinions about presidential candidate Donald Trump have been targeted relentlessly on social media by the candidate’s self-styled supporters; reporters who are Jewish (or are perceived to be Jewish) have borne the brunt of these attacks. There is evidence that Mr. Trump himself may have contributed to an environment in which reporters were targeted. Indeed, he repeatedly denounced reporters as “absolute scum,” and said of “most journalists” in December 2015, “I would never kill them, but I do hate them. And some of them are such lying, disgusting people. It’s true.” Accordingly, while we cannot (and do not) say that the candidate caused the targeting of reporters, we can say that he may have created an atmosphere in which
such targeting arose.

The social media attacks on journalists were brutal.

The Israeli-Palestinian conflict started on 7 October 2023, have resulted thus far to over 48,000 people killed including more than 17,000 children with a majority from Gaza, more than 30,000 people injured, over 10,000 missing, and over 1 million people displaced, fleeing conflict zones. The infrastructure damage includes the 87\% of housing units, 80\% of public buildings and 60\% of cropland 17 out of 36 hospitals, 68\% of road networks and 87\% of school buildings damaged. This conflict has as well launched an online discussion across various social media platforms. Telegram was no exception due to its encrypted communication and highly involved audience. The current study will cover an analysis of the related discussion in relation to different participants of the conflict and sentiment represented in those discussion. To this end, we prepared a dataset of 125K messages shared on channels in Telegram spanning from 23 October 2025 until today. Additionally, we apply the same analysis in two publicly available datasets from Twitter containing 2001 tweets and from Reddit containing 2M opinions. We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis in order to extract common themes or topics. Next, we apply sentiment analysis to analyze the emotional tone of the discussions. Our findings hint at polarized narratives as the hallmark of how political factions and outsiders mold public opinion. We also analyze the sentiment-topic prevalence relationship, detailing the trends that may show manipulation and attempts of propaganda by the involved parties. This will give a better understanding of the online discourse on the Israel-Palestine conflict and contribute to the knowledge on the dynamics of social media communication during geopolitical crises.

VOX-Pol Blog Post.

An analysis of Harakat al-Shabab’s multimedia releases and the rapid evolution in their production quality and design over a period of two to three years. Sound quality, animation, syncing sound with visuals, and narrative structures have all improved from the group’s multimedia releases from 2007 and 2008 when its videos were relatively simple, often just individuals sitting in front of a video camera, possibly just a camcorder, and grainy battle footage depicting fierce firefights between Harakat al-Shabab and the interim Somali government and its chief military backers, the African Union expeditionary force stationed inside the country.

The Somali jihadi-insurgent movement Al-Shabab has established itself, since emerging in 2007 after the overthrow of the Islamic Courts Union (ICU) umbrella in the wake of the December 2006 Ethiopian invasion and occupation of parts of Somalia, as one of the relatively few jihadi organizations to succeed in the capture, control, and governance of territory for a significant period of time. When Islamic State was masquerading as a «paper state» in 2008 and 2009, Al-Shabab’s leadership was busy constructing a bureaucracy of power, divided into regional and local nodes of authority, designed to implement and maintain insurgent rule over rapidly expanding territories. In establishing itself as a self-proclaimed and seemingly viable alternative governing authority, even if only in the short to medium term, Al Shabab continues to present a major challenge to the internationally recognized but weak and corrupt Somali Federal Government (SFG), the African Union Mission in Somalia (AMISOM), and the international community. The Somali insurgents, in successfully implementing a form of law and order, however harsh and philistine their interpretation of Islamic law and specifically their imposition of hudud («set») punishments for crimes such as highway robbery, banditry, theft, zina (various forms of fornication), and murder, provided other Sunni jihadi groups with an example of how jihadi-insurgent governance can be enacted in practice.

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.